Author: admin

Luangcharoenrat, C., Intrachooto, S., Peansupap, V. & Sutthinarakorn, W. Factors Influencing Construction Waste Generation in Building Construction: Thailand’s Perspective, Sustainability 2019, Vol. 11, Page 3638 11 3638. (2019). https://doi.org/10.3390/SU11133638

Tafesse, S., Girma, Y. E. & Dessalegn, E. Analysis of the socio-economic and environmental impacts of construction waste and management practices. Heliyon 8, e09169. https://doi.org/10.1016/J.HELIYON.2022.E09169 (2022).

Google Scholar 

Solís-Guzmán, J., Marrero, M., Montes-Delgado, M. V. & Ramírez-de-Arellano, A. A Spanish model for quantification and management of construction waste. Waste Manage. 29, 2542–2548. https://doi.org/10.1016/J.WASMAN.2009.05.009 (2009).

Google Scholar 

Yuan, H., Chini, A. R., Lu, Y. & Shen, L. A dynamic model for assessing the effects of management strategies on the reduction of construction and demolition waste. Waste Manage. 32, 521–531. https://doi.org/10.1016/J.WASMAN.2011.11.006 (2012).

Google Scholar 

Jaradat, H., Alshboul, O. A. M., Obeidat, I. M. & Zoubi, M. K. Green building, carbon emission, and environmental sustainability of construction industry in jordan: Awareness, actions and barriers. Ain Shams Eng. J. 15, 102441. https://doi.org/10.1016/J.ASEJ.2023.102441 (2024).

Google Scholar 

Olabi, A. G. et al. The role of green buildings in achieving the sustainable development goals. Int. J. Thermofluids. 25, 101002. https://doi.org/10.1016/J.IJFT.2024.101002 (2025).

Google Scholar 

Chang, D., Lee, C. K. M. & Chen, C. H. Review of life cycle assessment towards sustainable product development. J. Clean. Prod. 83, 48–60. https://doi.org/10.1016/J.JCLEPRO.2014.07.050 (2014).

Google Scholar 

Campo Gay, I., Hvam, L., Haug, A., Huang, G. Q. & Larsson, R. A digital tool for life cycle assessment in construction projects. Developments Built Environ. 20, 100535. https://doi.org/10.1016/J.DIBE.2024.100535 (2024).

Google Scholar 

Yin, Q. et al. Sustainability, A, Vol. 16, Page 7805 16 (2024) 7805. (2024). https://doi.org/10.3390/SU16177805

Cha, G. W., Choi, S. H., Hong, W. H. & Park, C. W. Developing a prediction model of Demolition-Waste Generation-Rate via principal component analysis. Int. J. Environ. Res. Public. Health. 20, 3159. https://doi.org/10.3390/IJERPH20043159 (2023).

Google Scholar 

Samal, C. G., Biswal, D. R., Udgata, G. & Pradhan, S. K. Estimation, Classification, and prediction of construction and demolition waste using machine learning for sustainable waste management: A critical review. Constr. Mater. 2025. 5 (Page 10 5), 10. https://doi.org/10.3390/CONSTRMATER5010010 (2025).

Google Scholar 

Zheng, L., Mueller, M., Luo, C. & Yan, X. Predicting whole-life carbon emissions for buildings using different machine learning algorithms: A case study on typical residential properties in Cornwall, UK. Appl. Energy. 357, 122472. https://doi.org/10.1016/J.APENERGY.2023.122472 (2024).

Google Scholar 

El-Kenawy, E. S. M. et al. Smart City Electricity Load Forecasting Using Greylag Goose Optimization-Enhanced Time Series Analysis, Arab. J. Sci. Eng. https://doi.org/10.1007/S13369-025-10647-3. (2025).

Saqr, A. E. S., Saraya, M. S. & El-Kenawy, E. S. M. Enhancing CO2 emissions prediction for electric vehicles using Greylag Goose optimization and machine learning. Sci. Rep. 2025. 15, 1. https://doi.org/10.1038/s41598-025-99472-0 (2025).

Google Scholar 

How Saudi Arabia is making the construction industry greener. and more sustainable, (n.d.). accessed April 29, (2025). https://www.arabnews.com/node/2587731/saudi-arabia

Cha, G. W. & Park, C. W. Development of an optimal machine learning model to predict CO2 emissions at the Building demolition Stage, buildings 2025, 15, Page 526 15 526. (2025). https://doi.org/10.3390/BUILDINGS15040526

Maged, A., Elshaboury, N. & Akanbi, L. Data-driven prediction of construction and demolition waste generation using limited datasets in developing countries: an optimized extreme gradient boosting approach. Environ. Dev. Sustain. 1–25. https://doi.org/10.1007/S10668-024-04814-Z/METRICS (2024).

Lu, W., Long, W. & Yuan, L. A machine learning regression approach for pre-renovation construction waste auditing. J. Clean. Prod. 397, 136596. https://doi.org/10.1016/J.JCLEPRO.2023.136596 (2023).

Google Scholar 

Hu, R., Chen, K., Chen, W., Wang, Q. & Luo, H. Estimation of construction waste generation based on an improved on-site measurement and SVM-based prediction model: A case of commercial buildings in China. Waste Manage. 126, 791–799. https://doi.org/10.1016/J.WASMAN.2021.04.012 (2021).

Google Scholar 

Gulghane, A., Sharma, R. L. & Borkar, P. A formal evaluation of KNN and decision tree algorithms for waste generation prediction in residential projects: a comparative approach. Asian J. Civil Eng. 25, 265–280. https://doi.org/10.1007/S42107-023-00772-5/METRICS (2024).

Google Scholar 

Guerra, B. C., Koo, H. J., Caldas, C. & Leite, F. Prediction of waste diversion and identification of trends in construction and demolition waste data using data mining. Int. J. Constr. Manage. 24, 374–383. https://doi.org/10.1080/15623599.2023.2235106 (2024). ;PAGE:STRING:ARTICLE/CHAPTER.

Google Scholar 

Cha, G. W., Moon, H. J. & Kim, Y. C. A hybrid machine-learning model for predicting the waste generation rate of Building demolition projects. J. Clean. Prod. 375, 134096. https://doi.org/10.1016/J.JCLEPRO.2022.134096 (2022).

Google Scholar 

Akanbi, L. A., Oyedele, A. O., Oyedele, L. O. & Salami, R. O. Deep learning model for demolition waste prediction in a circular economy. J. Clean. Prod. 274, 122843. https://doi.org/10.1016/J.JCLEPRO.2020.122843 (2020).

Google Scholar 

Lu, W. et al. Estimating construction waste generation in the greater Bay Area, China using machine learning. Waste Manage. 134, 78–88. https://doi.org/10.1016/J.WASMAN.2021.08.012 (2021).

Google Scholar 

Cha, G. W., Moon, H. J. & Kim, Y. C. Comparison of random forest and gradient boosting machine models for predicting demolition waste based on small datasets and categorical variables. Int. J. Environ. Res. Public. Health 2021. 18, 8530. https://doi.org/10.3390/IJERPH18168530 (2021).

Google Scholar 

Fang, Y., Lu, X. & Li, H. A random forest-based model for the prediction of construction-stage carbon emissions at the early design stage. J. Clean. Prod. 328, 129657. https://doi.org/10.1016/J.JCLEPRO.2021.129657 (2021).

Google Scholar 

Razi, N. & Ansari, R. A prediction-based model to optimize construction programs: considering time, cost, energy consumption, and CO2 emissions trade-off. J. Clean. Prod. 445, 141164. https://doi.org/10.1016/J.JCLEPRO.2024.141164 (2024).

Google Scholar 

Hou, Y. & Liu, S. Predictive modeling and validation of carbon emissions from china’s coastal construction industry: A BO-XGBoost ensemble Approach, sustainability 2024, 16, Page 4215 16 4215. (2024). https://doi.org/10.3390/SU16104215

Cha, G., Moon, H. & Kim, J. A method to improve the performance of support vector machine regression model for predicting demolition waste generation using categorical principal components analysis. Int. J. Sustainable Building Technol. Urban Dev. 2021. 12 (12), 3. https://doi.org/10.22712/SUSB.20210023 (2021).

Google Scholar 

Jafari, M. & Mousavi, E. Machine learning-based prediction of construction and demolition waste generation in developing countries: a case study. Environ. Sci. Pollut. Res. 1–12. https://doi.org/10.1007/S11356-024-34527-9/METRICS (2024).

Li, Q., Meng, Q., Cai, J., Yoshino, H. & Mochida, A. Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks. Energy Convers. Manag. 50, 90–96. https://doi.org/10.1016/J.ENCONMAN.2008.08.033 (2009).

Google Scholar 

Amasyali, K. & El-Gohary, N. M. A review of data-driven Building energy consumption prediction studies. Renew. Sustain. Energy Rev. 81, 1192–1205. https://doi.org/10.1016/j.rser.2017.04.095 (2018).

Google Scholar 

Olu-Ajayi, R., Alaka, H., Owolabi, H., Akanbi, L. & Ganiyu, S. Data-Driven Tools for Building Energy Consumption Prediction: A Review, Energies 2023, Vol. 16, Page 2574 16 2574. (2023). https://doi.org/10.3390/EN16062574

Ardabili, S., Abdolalizadeh, L., Mako, C., Torok, B. & Mosavi, A. Systematic review of deep learning and machine learning for Building energy. Front. Energy Res. 10, 786027. https://doi.org/10.3389/FENRG.2022.786027/BIBTEX (2022).

Google Scholar 

Venkatesh, V., Morris, M. G., Davis, G. B. & Davis, F. D. User acceptance of information technology: toward a unified view. MIS Q. 27, 425–478. https://doi.org/10.2307/30036540 (2003).

Google Scholar 

Davis, F. D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13, 319–339. https://doi.org/10.2307/249008 (1989).

Google Scholar 

Jeurissen, R. & Elkington, J. Cannibals With Forks: The Triple Bottom Line of 21st Century Business, Journal of Business Ethics 2000 23:2 23 229–231. (2000). https://doi.org/10.1023/A:1006129603978

Rogers, E. M. Diffusion of Innovations, 5th Edition (Google eBook), 576. (2003). https://books.google.com/books/about/Diffusion_of_Innovations_5th_Edition.html?id=9U1K5LjUOwEC (accessed August 18, 2025).

Darko, A., Chan, A. P. C., Owusu-Manu, D. G. & Ameyaw, E. E. Drivers for implementing green Building technologies: an international survey of experts. J. Clean. Prod. 145, 386–394. https://doi.org/10.1016/J.JCLEPRO.2017.01.043 (2017).

Google Scholar 

Breiman, L. Random forests. Mach. Learn. 45, 5–32. https://doi.org/10.1023/A:1010933404324/METRICS (2001).

Google Scholar 

Chen, T. & Guestrin, C. XGBoost: A scalable tree boosting system, Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 13-17-August-2016 785–794. (2016). https://doi.org/10.1145/2939672.2939785/SUPPL_FILE/KDD2016_CHEN_BOOSTING_SYSTEM_01-ACM.MP4

Ding, G. K. C. Sustainable construction—The role of environmental assessment tools. J. Environ. Manage. 86, 451–464. https://doi.org/10.1016/J.JENVMAN.2006.12.025 (2008).

Google Scholar 

Darko, A. et al. Review of application of analytic hierarchy process (AHP) in construction. Int. J. Constr. Manage. 19, 436–452. https://doi.org/10.1080/15623599.2018.1452098;SUBPAGE:STRING:ACCESS (2019).

Google Scholar 

Chan, A. P. C., Darko, A., Olanipekun, A. O. & Ameyaw, E. E. Critical barriers to green Building technologies adoption in developing countries: the case of Ghana. J. Clean. Prod. 172, 1067–1079. https://doi.org/10.1016/J.JCLEPRO.2017.10.235 (2018).

Google Scholar 

Alsanad, S. Awareness, Drivers, Actions, and barriers of sustainable construction in Kuwait. Procedia Eng. 118, 969–983. https://doi.org/10.1016/J.PROENG.2015.08.538 (2015).

Google Scholar 

Akadiri, P. O., Olomolaiye, P. O. & Chinyio, E. A. Multi-criteria evaluation model for the selection of sustainable materials for Building projects. Autom. Constr. 30, 113–125. https://doi.org/10.1016/J.AUTCON.2012.10.004 (2013).

Google Scholar 

Ding, Z., Zuo, J., Wu, J. & Wang, J. Y. Key factors for the BIM adoption by architects: A China study. Eng. Constr. Architectural Manage. 22, 732–748. https://doi.org/10.1108/ECAM-04-2015-0053/FULL/XML (2015).

Google Scholar 

Ochieng, E. G., Price, A. & Moore, D. Management of global construction projects. SSRN Electron. J. https://doi.org/10.2139/SSRN.3105473 (2018).

Google Scholar 

Richard & Fellows Anita. Liu, Research methods for construction, (2015). https://www.wiley.com/en-us/Research+Methods+for+Construction%2C+4th+Edition-p-9781118915738 (accessed April 29, 2025).

Tate, R., Beauregard, F., Peter, C. & Marotta, L. Pilot testing as a strategy to develop interview and questionnaire skills for scholar practitioners: A selection of education doctorate students’ reflective Vignettes, impacting education. J. Transforming Prof. Pract. 8, 20–25. https://doi.org/10.5195/IE.2023.333 (2023).

Google Scholar 

Kirchner, K., Zec, J. & Delibašić, B. Facilitating data preprocessing by a generic framework: a proposal for clustering. Artif. Intell. Rev. 45, 271–297. https://doi.org/10.1007/S10462-015-9446-6 (2016).

Google Scholar 

Prakash, A., Navya, N. & Natarajan, J. Big data preprocessing for modern world: opportunities and challenges. Lecture Notes Data Eng. Commun. Technol. 26, 335–343. https://doi.org/10.1007/978-3-030-03146-6_37 (2019).

Google Scholar 

Golazad, S. Z., Mohammadi, A., Rashidi, A. & Ilbeigi, M. From Raw to refined: data preprocessing for construction machine learning (ML), deep learning (DL), and reinforcement learning (RL) models. Autom. Constr. 168, 105844. https://doi.org/10.1016/J.AUTCON.2024.105844 (2024).

Google Scholar 

G.E.A.P.A. Batista, M. C. & Monard An analysis of four missing data treatment methods for supervised learning, Appl. Artif. Intell. 17 519–533. https://doi.org/10.1080/713827181;JOURNAL. (2003). :JOURNAL:UAAI20;REQUESTEDJOURNAL:JOURNAL:UAAI20;WGROUP:STRING:PUBLICATION.

Data Mining. Concepts and Techniques | ScienceDirect, (n.d.). accessed April 29, (2025). https://www.sciencedirect.com/book/9780123814791/data-mining-concepts-and-techniques

Opoku, A. & Fortune, C. Implementation of sustainable practices in UK construction organizations. Int. J. Sustain. Policy Pract. 8, 121–132. https://doi.org/10.18848/2325-1166/CGP/V08I01/55360 (2013).

Google Scholar 

Kernbach, J. M. & Staartjes, V. E. Foundations of machine Learning-Based clinical prediction modeling: part II—Generalization and overfitting. Acta Neurochir. Suppl. (Wien). 134, 15–21. https://doi.org/10.1007/978-3-030-85292-4_3 (2022).

Google Scholar 

Lipkovich, I., Ratitch, B. & Ivanescu, C. Statistical data mining of clinical data. Quant. Methods Pharm. Res. Development: Concepts Appl. 225–315. https://doi.org/10.1007/978-3-030-48555-9_6 (2020).

Kuhn, M. & Johnson, K. Applied predictive modeling. Appl. Predictive Model. 1–600. https://doi.org/10.1007/978-1-4614-6849-3/COVER (2013).

Mahmood, S. et al. Integrating machine and deep learning technologies in green buildings for enhanced energy efficiency and environmental sustainability. Sci. Rep. 14, 1–17. https://doi.org/10.1038/S41598-024-70519- (2024). Y;SUBJMETA=4066,685,704,844;KWRD=ENERGY+AND+SOCIETY,SUSTAINABILITY.

Google Scholar 

Rashid, K. et al. Machine learning and multicriteria analysis for prediction of compressive strength and sustainability of cementitious materials. Case Stud. Constr. Mater. 21, e04080. https://doi.org/10.1016/J.CSCM.2024.E04080 (2024).

Google Scholar 

Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46. https://doi.org/10.1111/J.1600-0587.2012.07348 (2013). .X;PAGE:STRING:ARTICLE/CHAPTER.

Google Scholar 

Awad, M. & Khanna, R. Support vector machines for classification. Efficient Learn. Machines. 39–66. https://doi.org/10.1007/978-1-4302-5990-9_3 (2015).

Liu, Y. & Song, H. Study on constructing support vector machine with granular computing. Procedia Eng. 15, 3098–3102. https://doi.org/10.1016/J.PROENG.2011.08.581 (2011).

Google Scholar 

, A. J., Smola, B. & Schölkopf A tutorial on support vector regression. Stat. Comput. 14, 199–222. https://doi.org/10.1023/B:STCO.0000035301.49549.88 (2004).

Google Scholar 

Tayefeh Hashemi, S., Ebadati, O. M. & Kaur, H. Cost Estimation and prediction in construction projects: a systematic review on machine learning techniques. SN Appl. Sci. 2, 1–27. https://doi.org/10.1007/S42452-020-03497-1/FIGURES/11 (2020).

Google Scholar 

Mai, H. V. T., Nguyen, T. A., Ly, H. B. & Tran, V. Q. Prediction compressive strength of concrete containing GGBFS using random forest model. Adv. Civil Eng. 2021, 6671448. https://doi.org/10.1155/2021/6671448 (2021).

Google Scholar 

Asadi, E., Da Silva, M. G., Antunes, C. H. & Dias, L. Multi-objective optimization for Building retrofit strategies: A model and an application. Energy Build. 44, 81–87. https://doi.org/10.1016/J.ENBUILD.2011.10.016 (2012).

Google Scholar 

Kiangala, S. K. & Wang, Z. An effective adaptive customization framework for small manufacturing plants using extreme gradient boosting-XGBoost and random forest ensemble learning algorithms in an industry 4.0 environment. Mach. Learn. Appl. 4, 100024. https://doi.org/10.1016/J.MLWA.2021.100024 (2021).

Google Scholar 

Al-Fakih, A., Al-wajih, E., Saleh, R. A. A. & Muhit, I. B. Ensemble machine learning models for predicting the CO2 footprint of GGBFS-based geopolymer concrete. J. Clean. Prod. 472, 143463. https://doi.org/10.1016/J.JCLEPRO.2024.143463 (2024).

Google Scholar 

Yaseen, Z. M., Ali, Z. H., Salih, S. Q. & Al-Ansari, N. Prediction of risk delay in construction projects using a hybrid artificial intelligence Model, sustainability 2020, 12, Page 1514 12 1514. (2020). https://doi.org/10.3390/SU12041514

ForouzeshNejad, A. A., Arabikhan, F. & Aheleroff, S. Optimizing Project Time and Cost Prediction Using a Hybrid XGBoost and Simulated Annealing Algorithm, Machines 2024, Vol. 12, Page 867 12 867. (2024). https://doi.org/10.3390/MACHINES12120867

Ji, S., Lee, B. & Yi, M. Y. Building life-span prediction for life cycle assessment and life cycle cost using machine learning: A big data approach. Build. Environ. 205, 108267. https://doi.org/10.1016/J.BUILDENV.2021.108267 (2021).

Google Scholar 

Fan, J. et al. Comparison of support vector machine and extreme gradient boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China. Energy Convers. Manag. 164, 102–111. https://doi.org/10.1016/J.ENCONMAN.2018.02.087 (2018).

Google Scholar 

Koo, B., La, S., Cho, N. W. & Yu, Y. Using support vector machines to classify Building elements for checking the semantic integrity of Building information models. Autom. Constr. 98, 183–194. https://doi.org/10.1016/J.AUTCON.2018.11.015 (2019).

Google Scholar 

Kumar, M. et al. Soft computing-based prediction models for compressive strength of concrete. Case Stud. Constr. Mater. 19, e02321. https://doi.org/10.1016/J.CSCM.2023.E02321 (2023).

Google Scholar 

Khan, A. A., Chaudhari, O. & Chandra, R. A review of ensemble learning and data augmentation models for class imbalanced problems: Combination, implementation and evaluation. Expert Syst. Appl. 244, 122778. https://doi.org/10.1016/J.ESWA.2023.122778 (2024).

Google Scholar 

Xia, Y., Jiang, S., Meng, L. & Ju, X. XGBoost-B-GHM: An Ensemble Model with Feature Selection and GHM Loss Function Optimization for Credit Scoring, Systems 2024, Vol. 12, Page 254 12 254. (2024). https://doi.org/10.3390/SYSTEMS12070254

Sathiparan, N., Jeyananthan, P. & Subramaniam, D. N. A comparative study of machine learning techniques and data processing for predicting the compressive strength of pervious concrete with supplementary cementitious materials and chemical composition influence, Next Mater. 9 100947. https://doi.org/10.1016/J.NXMATE.2025.100947. (2025).

Alkaissy, M. et al. Enhancing construction safety: machine learning-based classification of injury types. Saf. Sci. 162, 106102. https://doi.org/10.1016/J.SSCI.2023.106102 (2023).

Google Scholar 

Uddin, M. N., Ye, J., Deng, B., Li, L. & Yu, K. Interpretable machine learning for predicting the strength of 3D printed fiber-reinforced concrete (3DP-FRC). J. Building Eng. 72, 106648. https://doi.org/10.1016/J.JOBE.2023.106648 (2023).

Google Scholar 

Malakouti, S. M. From accurate to actionable: interpretable PM2.5 forecasting with feature engineering and SHAP for the Liverpool–Wirral region. Environ. Challenges. 21, 101290. https://doi.org/10.1016/J.ENVC.2025.101290 (2025).

Google Scholar 

Zhao, Q. et al. The influencing factors and future development of energy consumption and carbon emissions in urban households: A review of china’s experience. Appl. Sci. (Switzerland). 15, 2961. https://doi.org/10.3390/APP15062961/S1 (2025).

Google Scholar 

Zaman, K. Urban governance and power consumption dynamics in china’s carbon-intensive sectors: insights for sustainable development. Urban Gov. 4, 313–328. https://doi.org/10.1016/J.UGJ.2024.12.001 (2024).

Google Scholar